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Improvement in Bed Transferring

CBE ID
0175
1.4 Project
Advanced Illness and Post-Acute Care
Endorsement Status
Endorsed with Conditions
E&M Committee Rationale/Justification

When this measure comes back for maintenance, the committee would like to see: 

  • The developer explore, with their technical expert panel (TEP), combining the four improvement measures (CBE #0167, CBE #0174, CBE #0175, and CBE #0176) into a composite score, with the ability to identify individual scores for each of the four areas of improvement.
1.1 New or Maintenance
Maintenance
Previous Endorsement Cycle
Spring 2024
Is Under Review
No
Next Maintenance Cycle
Spring 2029
1.3 Measure Description

Percentage of home health episodes of care during which the patient got better at getting in and out of bed. This is a rate/proportion measure targeted at elderly individuals with multiple chronic conditions during home health quality of care episodes. 

    • 1.5 Measure Type
      Outcome
      1.6 Composite Measure
      No
      1.7 Electronic Clinical Quality Measure (eCQM)
      No
      1.8 Level Of Analysis
      Facility
      1.9 Care Setting
      Home Health
      1.10 Measure Rationale

      Patients need certain physical abilities to transfer safely from bed to chair (and chair to bed), or to turn and position themselves in bed if bedfast. Many patients who receive home health care are recovering from an injury or illness and may have difficulty with bed transferring, and/or may need help from another person or special equipment to accomplish this activity. Safe transferring is critical in being able to remain at home. The required physical abilities for bed transferring can be developed or improved by managing patient symptoms and through rehabilitative services. Home health care staff can encourage patients to be as independent as possible, can evaluate patients´ needs for, and can teach them how to use, special devices or equipment and increase their ability to perform some activities without the assistance of another person. Improving functional status related to bed transferring contributes to quality of life and can allow patients to live as long as possible in their own environment. Recovering independence in bed transferring is often a rehabilitative goal for home health patients, making it a reasonable evaluation indicator of effective and high-value home health care.

      1.11 Measure Webpage
      https://www.cms.gov/medicare/quality/home-health/home-health-quality-measures
      1.20 Testing Data Sources
      Standardized Patient Assessments
      1.25 Data Sources

      https://www.cms.gov/medicare/quality/home-health/oasis-data-sets

       

      The reporting of quality data by home health agencies (HHAs) is mandated by Section 1895(b)(3)(B)(v)(II) of the Social Security Act (“the Act”).  Outcome and Assessment Information Set (OASIS) reporting is mandated in the Medicare regulations at 42 C.F.R.§484.250(a), which requires HHAs to submit OASIS assessments to meet the quality reporting requirements of section 1895(b)(3)(B)(v) of the Act. It is important to note that to calculate quality measures from OASIS data, there must be a complete quality episode, which requires both a Start of Care (initial assessment) or Resumption of Care OASIS assessment and a Transfer or Discharge OASIS assessment.

    • 1.14 Numerator

      Number of home health episodes of care where the value recorded on the discharge assessment indicates less impairment in bed transferring at discharge than at start (or resumption) of care.

      1.14a Numerator Details

      The number of home health episodes for which the response to OASIS item M1850 (“Transferring”) on the discharge assessment indicates less impairment in bed transferring compared to start (or resumption) of care. This is determined by the following responses on the SOC/ROC [1] and EOC [2] assessments for the quality episode: M1850[2] < M1850[1].

    • 1.15 Denominator

      Number of home health episodes of care ending with a discharge during the reporting period, other than those covered by generic or measure-specific exclusions.

      1.15a Denominator Details

      Home health quality episodes ending with a discharge from the agency during the reporting period (M0100[2] = 09), except for those meeting the exclusion criteria.

    • 1.15b Denominator Exclusions

      All home health episodes of care for which the patient at start/resumption of care was able to transfer independently (M1850[1] = 00) or the patient was nonresponsive (M1700[1] = 04 or M1710[1] = NA or M1720[1] = NA) or the episode is covered by the generic exclusions (see following section).

      1.15c Denominator Exclusions Details

      Home health episodes of care for which (1) at start/resumption of care OASIS item M1850 = 0, indicating the patient was able to transfer self independently; or (2) at start/resumption of care, OASIS item M1700 "Cognitive Functioning" is 4, or M1710 "When Confused" is NA, or M1720 "When Anxious" is NA, indicating the patient is non-responsive; or (3) the patient did not have a discharge assessment because the episode of care ended in transfer to inpatient facility or death at home; or (4) the episode is covered by one or more of the generic exclusions:


      a. Pediatric home health patients (less than 18 years of age).
      b. Home health patients receiving maternity care only.
      c. Home health patients receiving non-skilled care only.
      d. Home health patients for which neither Medicare nor Medicaid are a payment source.
      e. The episode of care does not end during the reporting period.
      f. If the home health agency sample includes fewer than 20 episodes after all other patient-level exclusions are applied, or if the agency has been in operation less than six months, then the data is suppressed from public reporting on Home Health Compare.
      g. Hospice exclusion: Episodes of care that end in a non-institutional hospice on or after January 1, 2023 are excluded: M2420 “Discharge Disposition” is 3 and M0100 “Reason For Assessment” is 9.

       

      Table 1 (see supplemental attachment page 3) provides the episode counts by exclusion criterion for episodes of care that started and ended in CY 2022. In CY 2022, 1,683,168 episodes of care were excluded from the denominator for Improvement in Bed Transferring (#0175) due to meeting at least one exclusion criterion. Approximately 94,000 episodes of care in CY 2022 ended in a discharge to non-institutional hospice. This exclusion criterion is only applicable to episodes of care ending on or after January 1, 2023 and is therefore not listed in Table 1 (see supplemental attachment page 3).

    • OLD 1.12 MAT output not attached
      Attached
      1.13a Data dictionary not attached
      Yes
      1.16 Type of Score
      Rate/proportion
      1.17 Measure Score Interpretation
      Better quality = Higher score
      1.18 Calculation of Measure Score

      1. Define an episode of care (the unit of analysis): Data from matched pairs of OASIS assessments for each episode of care (start or resumption of care paired with a discharge or transfer to inpatient facility) are used to calculate individual patient outcome measures.
      2. Identify target population: All quality episodes of care ending during a specified time interval (usually a period of twelve months), subject to generic and measure-specific exclusions. Cases meeting the target outcome are those where the patient is more independent in bed transferring at discharge than at start/resumption of care:M1850_CRNT_TRNSFRING [2] < M1850_CRNT_TRNSFRING [1].
      3. Aggregate the Data: The observed outcome measure value for each home health agency is calculated as the percentage of cases meeting the target population (denominator) criteria that meet the target outcome (numerator)criteria.
      4. Risk Adjustment: The expected probability for a patient is calculated using the following formula:

      𝑃(𝑥) = 1/(1 + 𝑒−(𝑎+Σ 𝑏𝑖𝑥𝑖))
      Where:
           P(x) = predicted probability of achieving outcome x
           a = constant parameter listed in the model documentation
           bi = coefficient for risk factor i in the model documentation
           xi = value of risk factor i for this patient
      Predicted probabilities for all patients included in the measure denominator are then averaged to derive an expected outcome value for the home health agency. This expected value is then used, together with the observed (unadjusted) outcome value and the expected value for the national population of patients for the same data collection period, to calculate a risk-adjusted outcome value for the home health agency. The formula for the adjusted value of the outcome measure is as follows:
      𝑋(𝐴𝑟𝑎)=𝑋(𝐴obs)+ 𝑋(𝑁exp)−𝑋(𝐴𝑒xp)
      Where:
           X(Ara) = Agency risk-adjusted outcome measure value
           X(Aobs) = Agency observed outcome measure value
           X(Aexp) = Agency expected outcome measure value
           X(Nexp) = National expected outcome measure value


      If the result of this calculation is a value greater than 100%, the adjusted value is set to 100%. Similarly, if the result is a negative number the adjusted value is set to zero.

      1.19 Measure Stratification Details

      The measure is not stratified.

      1.26 Minimum Sample Size

      Not applicable

    • 2.1 Attach Logic Model
      0175_Logic Model.pdf
      2.2 Evidence of Measure Importance

      Needing assistance with activities of daily living is both necessary and common among older adults. Worldwide, close to 20% of older adults living at home report needing some form of assistance with their activities of daily living (ADLs), and in the US 29% of older adults report difficulties completing their ADLs (Chen et al., 2022). A large part of work and support in home health is around safety in the home and the safe performance of ADLs including transferring in and out of bed. Home health care staff can encourage patients to be as independent as possible, can evaluate patients´ needs, and can teach them how to use special devices or equipment that assist them in the process of safely getting in and out of bed. The use of assistive devices that help with safe function is often the target of home health interventions and occupational therapy interventions can improve health management by providing teaching and education related to the use of assistive devices and the safe performance of ADLs and IADLs (Foster et al., 2021) The proper use of assistive devices such as slide boards and bed side commodes can lead to the safe completion of bed transfers, and have been shown to decrease the need for additional personal assistance for older adults living at home (Anderson & Wiener, 2015). Improving functional status related to bed transferring contributes to quality of life and can allow patients to live as long as possible in their own environment. Recovering independence in bed transferring is often a rehabilitative goal for home health patients, making it a reasonable evaluation indicator of effective and high-value home health care.


      Anderson, W. L., & Wiener, J. M. (2015). The impact of assistive technologies on formal and informal home care. The Gerontologist, 55(3), 422-433.


      Chen, S., Jones, L. A., Jiang, S., Jin, H., Dong, D., Chen, X., . . . Zhu, A. (2022). Difficulty and help with activities of daily living among older adults living alone during the COVID-19 pandemic: a multi-country population-based study. BMC geriatrics, 22(1), 1-14.


      Foster, E. R., Carson, L. G., Archer, J., & Hunter, E. G. (2021). Occupational therapy interventions for instrumental activities of daily living for adults with Parkinson’s disease: A systematic review. The American Journal of Occupational Therapy, 75(3).

    • 2.6 Meaningfulness to Target Population

      The public can comment on the home health quality reporting program when notice of proposed rulemaking is published as well as through the consensus-based entity (CBE) public commenting. No comments have been received in this time period regarding this measure. The target of this performance-based measure is the Medicare-certified home health agency. Functional status was confirmed as a domain of importance for quality measurement at a recent Technical Expert Panel (TEP).

    • 2.4 Performance Gap

      Improvement in Bed Transferring (#0175) is calculated using CMS’s Home Health Quality Reporting Program’s assessment tool, the Outcome and Assessment Information Set (OASIS). All components of the measure are defined using data from the OASIS, including the numerator, denominator, exclusions, and risk factors. The measure is risk adjusted to account for patient characteristics at the start of care or resumption of care (SOC/ROC). The denominator consists of unique quality episodes, i.e. a SOC/ROC assessment paired with an end of care (EOC) assessment.


      While all the data used to report results in this form are derived from the OASIS, the periods used to generate results vary. Trends are presented from calendar year 2019 (CY 2019) to CY 2022. We restrict descriptive characteristics, reliability, and validity to CY 2022, the most recent calendar year of data currently available. The results generated for risk adjustment use CY 2021 data, the data used during the most recent maintenance reevaluation and risk adjustment update.


      Table 2 (see supplemental attachment page 8) presents performance for Improvement in Bed Transferring (#0175) from CY 2019 to CY 2022 among home health agencies that exceed the public reporting threshold of at least 20 quality episodes of care. Overall, mean performance has been trending upwards, with a low of 0.774 in CY 2019 and a high of 0.810 in CY 2022. The lower and upper bounds of the interquartile range have also increased with each year. Despite the steady increases year-over-year, there remains a performance gap for Improvement in Bed Transferring (#0175). Fewer than 70 percent of quality episodes exhibit improvement among the lowest quartile of home health agencies, and between 20 and 25 percent of quality episodes fail to improve for the average home health agency (see Tables 2 and 3 in supplemental attachment page 8).


      Aside from CY 2020, which was affected by the COVID public health emergency reporting requirements, roughly 4.3 million quality episodes are used to score the measure. The measure is publicly reported for over 7,000 home health agencies.

       

      Table 3 (see supplemental attachment page 8) presents performance for Improvement in Bed Transferring (#0175) for CY 2022 by home health agency size among home health agencies that exceed the public reporting threshold. From bottom decile to top decile, the distribution in performance is tight with a minimum at Decile 1 of 0.695 and a maximum at Decile 9 of 0.875, a 0.18 difference in mean score. Smaller home health agencies perform worse on the measure, with Deciles 1 to 4 performing lower than the overall mean score and Deciles 5 to 10 performing higher.

      Table 1. Performance Scores by Decile
      Performance Gap
      Overall Minimum Decile_1 Decile_2 Decile_3 Decile_4 Decile_5 Decile_6 Decile_7 Decile_8 Decile_9 Decile_10 Maximum
      Mean Performance Score 0.810 0.021 0.695 0.740 0.757 0.777 0.813 0.841 0.858 0.870 0.875 0.874 1.000
      N of Entities 7601 765 769 762 750 759 760 758 758 763 757
      N of Persons / Encounters / Episodes 4,382,987 20,930 37,684 58,795 87,591 133,146 198,501 297,497 461,400 779,294 2,308,149
    • 3.1 Feasibility Assessment

      This is a long-standing measure in the Home Health Quality Reporting Program. We have not identified any feasibility issues for this measure. The Outcome and Assessment Information Set (OASIS) items for this measure must be completed as part of the OASIS assessment. Responses to the questions are required for data submission to the CMS system. The OASIS burden estimates are reported  through rulemaking.

      3.3 Feasibility Informed Final Measure

      OASIS data collection and submission are a requirement of the Medicare Home Health Conditions of Participation. Functional assessment is conducted as part of usual clinical practice, and information on patients’ bed transferring status used to calculate this measure is recorded in the relevant OASIS items embedded in the home health agency’s clinical assessment. OASIS data are collected by the home health agency during the care episode and submitted electronically to CMS via the Internet Quality Improvement and Evaluation System (iQIES). No issues regarding availability of data, missing data, timing or frequency of data collection, patient confidentiality or implementation have become apparent since OASIS-E was implemented January 1, 2023.No changes have been made to the measure specifications.

    • 3.4 Proprietary Information
      Not a proprietary measure and no proprietary components
    • 4.1.3 Characteristics of Measured Entities

      Table 4 (see supplemental attachment page 10) identifies the publicly reporting home health agencies by size and Census region. This distribution of home health agencies is used for reliability and validity testing. 7,601 home health agencies have 20 or more quality episodes starting and ending in CY 2022. 

      4.1.1 Data Used for Testing

      While all the data used to report results in this form are derived from the OASIS, the periods used to generate results vary. We restrict descriptive characteristics, reliability, and validity to CY 2022, the most recent calendar year of data currently available, and the results generated for exclusions and risk adjustment use CY 2021 data, the data used during the most recent maintenance reevaluation and risk adjustment update.

      4.1.4 Characteristics of Units of the Eligible Population

      Table 5 (see supplemental attachment page 10) identifies the patient characteristics of quality episodes treated by publicly reporting home health agencies. Characteristics are reported by sex, race, age, and Census region. This distribution of quality episodes is used for reliability and validity testing. 4,382,987 quality episodes started and ended in CY2022 and met the denominator exclusion and public reporting requirements. 

      4.1.2 Differences in Data

      See answers to other 4.1 questions. 

    • 4.2.1 Level(s) of Reliability Testing Conducted
      Person– or encounter–level (i.e., data element) (e.g., inter–abstractor reliability)
      Accountable Entity-Level (e.g., signal-to-noise analysis)
      4.2.2 Method(s) of Reliability Testing

      Below, we address reliability at two levels: (1) the performance measure and (2) the underlying data element: OASIS item M1850 (Transferring: Current ability to move safely from bed to chair, or ability to turn and position self in bed if patient is bedfast.).


      Reliability of the Performance Measure Score: Abt measured the extent to which differences in each quality measure were due to actual differences in agency performance versus variation that arises from measurement error. Statistically, reliability depends on performance variation for a measure across agencies, the random variation in performance for a measure within an agency’s panel of attributed beneficiaries, and the number of beneficiaries attributed to the agency. High reliability for a measure suggests that comparisons of relative performance across agencies are likely to be stable over different performance periods, and that the performance of one agency on the quality measure can confidently be distinguished from another. Potential reliability values range from zero to one, where one (highest possible reliability) means that all variation in the measure’s rates is the result of variation in differences in performance across agencies, while zero (lowest possible reliability) means that all variation is a result of measurement error.


      To assess measure reliability, Abt used a split-half reliability test. First, we randomly divided each publicly reporting home health agency’s quality episodes into two separate equally sized groups. Then, we calculated risk-adjusted performance rates for each group. Then, using the paired performance rates, we calculated the absolute agreement intra-class correlation statistic or ICC(2,1) with a Spearman-Brown correction to address the artificial reduction in home health agency size by half. Additionally, we recalculate ICC(2,1) within each agency size decile, where size is measured as the number of quality episodes treated after denominator and public reporting exclusions.


      Reliability of the Underlying Data Element: The measure is calculated by comparing patient functioning at the start and end of a home health quality episode, as reported by the home health OASIS data set. Patient ability to ambulate is based on response to OASIS item M1850 (Transferring: Current ability to move safely from bed to chair, or ability to turn and position self in bed if patient is bedfast.):
      0. Able to independently transfer.
      1. Able to transfer with minimal human assistance or with use of an assistive device. 2. Able to bear weight and pivot during the transfer process but unable to transfer self.
      2. Unable to transfer self and is unable to bear weight or pivot when transferred by another person.
      3. Bedfast, unable to transfer but is able to turn and position self in bed.

      4. Bedfast, unable to transfer and is unable to turn and position self.


      In 2016 and 2017, Abt and partners conducted a field test of new and existing OASIS items on 12 home health agencies in four states for 213 home health patients.1 Home health registered nurses and physical therapists, trained by the study team, collected data during home visits at start of care (SOC) or resumption of care (ROC), and/or at discharge. Follow-up visits were conducted within 24 hours of the initial field test visit, by a different registered nurse or physical therapist to test interrater reliability. M1850 was one of the existing OASIS items that was tested. Interrater reliability was assessed for SOC or ROC and at Discharge with a linear weighted kappa. The number patients for which inter-rater reliability could be tested was 104 at SOC/ROC and 83 at discharge.\


      The kappa statistic is generally considered to be the “gold standard” statistic associated with item reliability as it factors in the possibility of chance agreement. Kappa values are reported as decimal values between 0.00 (poor) and 1.00 (perfect). These can be interpreted using the following seven categories:2
      − Poor < 0.10
      − Slight = 0.10 to 0.20
      − Fair = 0.21 to 0.40
      − Moderate = 0.41 to 0.60
      − Substantial = 0.61 to 0.80
      − Near perfect = 0.81 to 0.99
      − Perfect = 1.00

       

      1 Abt Associates (2018). “OASIS Field Test Summary Report: Outcome and Assessment Information Set (OASIS) Quality Measure Development and Maintenance Project.”
      2 Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics, 1977. 33(1):159-174.

      4.2.3 Reliability Testing Results

      Reliability of the Performance Measure Score: The table summarizes the distribution of reliability scores for the 7,601 home health agencies exceeding the public reporting threshold of at least 20 eligible quality episodes of care.

       

      Reliability of the Underlying Data Element: The inter-rater reliability (weighted kappa) values for M1850 Transferring was 0.42 at SOC/ROC and 0.45 at discharge.

      Table 2. Accountable Entity–Level Reliability Testing Results by Denominator-Target Population Size
      Accountable Entity-Level Reliability Testing Results
      &nbsp; Overall Minimum Decile_1 Decile_2 Decile_3 Decile_4 Decile_5 Decile_6 Decile_7 Decile_8 Decile_9 Decile_10 Maximum
      Reliability 0.939 0.872 0.907 0.940 0.954 0.954 0.958 0.972 0.969 0.979 0.987
      Mean Performance Score 0.810 0.021 0.695 0.740 0.757 0.777 0.813 0.841 0.858 0.870 0.875 0.874 1.000
      N of Entities 7,601 765 769 762 750 759 760 758 758 763 757
      N of Persons / Encounters / Episodes 4,382,987 20,930 37,684 58,795 87,591 133,146 198,501 297,497 461,400 779,294 2,308,149
      4.2.4 Interpretation of Reliability Results

      Reliability of the Performance Measure Score: The ICC(2,1) statistics exceed 0.800, even within the decile with the smallest home health agencies, suggesting strong reliability and acceptability for drawing inferences about home health agencies.


      Reliability of the Underlying Data Element: Based on the weighted kappa statistics the inter-rater reliability indicated moderate agreement at SOC/ROC (0.42) and moderate agreement at discharge (0.45). Given the scale of the response to this OASIS item (six possible responses), we conclude that the item achieves sufficient reliability.

    • 4.3.1 Level(s) of Validity Testing Conducted
      Person– or encounter–level (i.e., data element) (e.g., sensitivity and specificity)
      Accountable entity–level (e.g., criterion validity)
      4.3.2 Type of accountable entity-level validity testing conducted
      Empirical validity testing at the accountable entity-level (e.g., criterion validity, construct validity, known groups analysis)
      4.3.3 Method(s) of Validity Testing

      Below, we address validity at two levels: (1) the performance measure and (2) the underlying data element - OASIS item M1850 (Transferring: Current ability to move safely from bed to chair, or ability to turn and position self in bed if patient is bedfast.).


      Validity of the Performance Measure Score: Abt assessed the convergent validity of the measure. Convergent validity refers to the extent to which measures that are designed to assess the same construct are related to each other. To evaluate the convergent validity of the measure, Abt calculated the Spearman rank correlations of the Improvement in Bed Transferring (#0175) measure with other relevant OASIS-based measures and the fee-for-service (FFS) claims-based measure Discharge to Community (#3477) measure.


      The Spearman rank correlation assesses the statistical dependence between the rankings of two variables. In our case, we rank home health agencies according to the Improvement in Bed Transferring (#0175) measure and other home health agency-level measures. High correlation or association between the Improvement in Bed Transferring (#0175) measure and other functional measures of improvement would be expected and desired. Low correlation would indicate that the measure may not be valid (is not measuring what we think it is measuring). We only expect a positive correlation with Discharge to Community (#3477), as the population differs by payer (FFS versus FFS, Medicare Advantage, and Medicaid) and the numerator criteria measure different outcomes (successful discharge to community versus improvement in function).

       

      Validity of the Underlying Data Element: The OASIS item M1850: Transferring has been used continuously as part of the OASIS since 2001. The behaviorally benchmarked responses were updated and improved based on input from clinicians and technical experts. The OASIS instrument has been published in the Federal Register for comment (both items and measures based off those items) and no objections or suggestions for revision have been noted regarding the response options.
       

      The original OASIS item was originally carefully designed for measuring and ultimately enhancing patient outcomes as part of the National OBQI Demonstration project (1995 – 2000). OASIS items were derived by first specifying a set of patient outcomes considered critical by home care experts (e.g., nurses, physicians, therapists, social workers, administrators) for evaluating the effectiveness of care. These outcomes were chosen from the most important domains of health status addressed by home care providers. OASIS data items were developed, tested in hundreds of agencies, and refined for measuring outcomes to evaluate and enhance the effectiveness of home care. OASIS data items and measurement methods were reviewed by multidisciplinary panels of research methodologists, clinicians, home care managers, and policy analysts. Several tests of validity were conducted for each OASIS item, including Ambulation/Locomotion. Validity testing included:
      1) Consensus validity by expert researcher/clinical panels for outcome measurement and risk factor measurement
      2) Consensus validity by expert clinical panels for patient assessment and care planning
      3) Criterion or convergent/predictive validity for outcome measurement/risk factor measurement
      4) Convergent/predictive validity: case mix adjustment for payment
      5) Validation by patient assessment and care planning


      Descriptions for these validation assessments are taken from the “Volume 4: OASIS Chronicle and Recommendation” OASIS and Outcome-Based Quality Improvement in Home Health Care, November 2001, Center for Health Services Research, University of Colorado Health Sciences Center, Denver, CO.

      4.3.4 Validity Testing Results

      Validity of the Performance Measure Score: Table 7 (see supplemental attachment page 14) shows the Spearman rank correlations of the Improvement in Bed Transferring (#0175) measure with other publicly reported measures of home health quality derived from OASIS assessments and Medicare Fee-for-Service (FFS) claims.

       

      Validity of the Underlying Data Element: As noted above,
      1. Consensus validity: The item was reviewed by panels of researchers and clinicians and was recommended for measuring patient outcomes relevant to home health care provision and quality measurement, or for risk adjustment of outcome analyses.

      2. Consensus validity by expert clinical panels for patient assessment and care planning: The item was reviewed by a panel of clinical experts and was recommended for inclusion in a core set of data items for patient assessment and care planning.

      3. Criterion or convergent/predictive validity for outcome measurement/risk factor measurement: The item was tested empirically for use in conjunction with outcome measures or risk factors predictive of patient outcomes. The item was found to be related to other indicators of health status and patient outcomes in a statistically significant and clinically meaningful way.

      4. Convergent/predictive validity: Case-mix adjustment for payment: The item was tested and is used in the grouping algorithm that, in part, determines the per-episode payment to home health agencies for care provided under the Medicare home health benefit.

      5. Validation by patient assessment and care planning: The item has been used by clinicians for patient assessment and care planning in several hundred home health agencies and has been reported by practicing clinicians to be effective and useful for these purposes.


      Results of these validation assessments are taken from the “Volume 4: OASIS Chronicle and Recommendation” OASIS and Outcome-Based Quality Improvement in Home Health Care, November 2001, Center for Health Services Research, University of Colorado Health Sciences Center, Denver, CO.

      4.3.5 Interpretation of Validity Results

      Validity of the Performance Measure Score: As detailed in Table 7 (see supplemental attachment page 14), Improvement in Bed Transferring (#0175) measure displays a statistically significant positive correlation with several publicly reported measures that similarly assess patient functioning and Discharge to Community (#3477), which lends evidence to the measure’s validity. It may be that strong performance on the other OASIS-based measures directly leads to an improvement in bed transferring. It may also be the case that high quality agencies perform well on both the Improvement in Bed Transferring (#0175) measure and other OASIS-based measures of patient functioning and communication due to cultural or organization-level factors.

      Validity of the Underlying Data Element: Item validity was established based on results of testing described above. In addition, the item was also reviewed as part of the OMB/PRA review process for the most recent OASIS data set revision which allowed for two national comment periods (60 days and 30 days) wherein the face validity of the item was supported by the comments received.

    • 4.4.1 Methods used to address risk factors
      Statistical risk model with risk factors
      4.4.2 Conceptual Model Rationale

      A patient’s improvement in bed transferring is dependent on a variety of factors, including social risk factors, clinical and behavioral risk factors, and access to care. The conceptual model shown in Figure 2 (see supplemental attachment page 17) is inspired by a similar conceptual model proposed by the Committee on Accounting for Socioeconomic Status in Medicare Payment Programs. In this conceptual model, social risk factors influence access to care and clinical and behavioral risk factors, as well as the measure itself. In turn, clinical and behavioral risk factors influence health care and resource use, access to care, and the measure itself. Access to care only influences health care and resource use. Finally, the home health agency affects the measure through interventions like skilled nursing, therapy, and care coordination. These interventions may be able to address some social, clinical, or behavioral risk factors, in part if not fully.


      Improvement in Bed Transferring (#0175) attempts to measure a home health agency’s ability to improve patient bed transferring while the patient is in its care; however, because certain factors are outside of its control, we risk-adjust the measure. Risk adjustment is used to promote incentives for home health agencies to provide the same care to patients regardless of patient characteristics at SOC/ROC.


      The risk factors that can be fully addressed should not be included in the risk adjustment model because the home health agency is expected to be responsible for addressing that risk factor. For instance, if all other risk factors are identical, a home health agency is expected to provide two patients with identical quality care regardless of race or ethnicity.


      By contrast, a patient who is living alone will have different needs than a patient who lives in a congregate setting. While a home health agency is expected to adapt its care to different living situations, it is not expected to address all the needs for a patient living alone, like having professionals on staff readily available during emergencies. Similarly, a home health agency is not expected to influence the patient’s clinical and behavioral status at SOC/ROC. As a result, relevant clinical and behavioral risk factors to bed transferring are included in the risk adjustment model.

      4.4.3 Risk Factor Characteristics Across Measured Entities

      Table 8 (see supplemental attachment pages 17-20) shows the mean and standard deviation of the observed value for Improvement in Bed Transferring (#0175) by risk factor in CY2022. 

      4.4.4 Risk Adjustment Modeling and/or Stratification Results

      The risk adjustment methodology used is based on logistic regression analysis which results in a statistical prediction model for each outcome measure.  For each patient who is included in the denominator of the outcome measure, the model is used to calculate the predicted probability that the patient will experience the outcome. The predicted probability for a patient is calculated using the following formula:

      𝑃(𝑥) = 1/(1 + 𝑒−(𝑎+Σ 𝑏𝑖𝑥𝑖))

      Where:

      P(x) = predicted probability of achieving outcome x 

      a = constant parameter listed in the model documentation 

      bi = coefficient for risk factor i in the model documentation 

      xi = value of risk factor i for this patient

      Predicted probabilities for all patients included in the measure denominator are then averaged to derive an expected outcome value for the agency.  This expected value is then used, together with the observed (unadjusted) outcome value and the expected value for the national population of patients for the same data collection period, to calculate a risk-adjusted outcome value for the home health agency. The formula for the adjusted value of the outcome measure is as follows:

      𝑋(𝐴𝑟𝑎)=𝑋(𝐴obs)+ 𝑋(𝑁exp)−𝑋(𝐴𝑒xp)

      Where:

      X(Ara) = Agency risk-adjusted outcome measure value 

      X(Aobs) = Agency observed outcome measure value 

      X(Aexp) = Agency expected outcome measure value 

      X(Nexp) = National expected outcome measure value

      If the result of this calculation is a value greater than 100%, the adjusted value is set to 100%. Similarly, if the result is a negative number the adjusted value is set to zero.

       

      For a more detailed summary of risk adjustment specifications including definitions of the risk factors, please consult the Home Health Quality Reporting Program Risk Adjustment Technical Specifications 2024 (PDF).[1]

       

      The risk adjustment model was developed using OASIS national repository data from assessments submitted between January 1, 2021, and December 31, 2021 (~6.2 million quality episodes). The population of 6.2 million quality episodes for calendar year 2021 was split in half such that 3.1 million quality episodes were used as a developmental sample and 3.1 million quality episodes were used as a validation sample. The following process was used to identify unique contributing risk factors to the prediction model: 

       

      1. Risk factors were identified based on OASIS items that will remain or will be added following the transition to OASIS-E. The statistical properties of the items were examined to specify risk factors (e.g., item responses were grouped when there was low prevalence of certain responses). Team clinicians then reviewed all risk factors for clinical relevance and redefined or updated risk factors as necessary. These risk factors were divided into 31 content focus groups (e.g., functional status, Hierarchical Condition Categories, etc.). Where possible, risk factors were defined such that they flagged mutually exclusive subgroups within each content focus group. When modelling these risk factors, the exclusion category was set to be either the risk factor flag for most independent or the most frequent within each content focus group. 
      2. A logistic regression specification was used to estimate coefficients among the full set of candidate risk factors. Those risk factors that are statistically significant at probability <0.0001 are flagged for further review in Step 3.
      3. Each risk factor flagged in Step 2 was reviewed to determine which one of the two groups its content focus group resided. Either its content focus group was explicitly tiered by increasing severity or it was not. This classification determined which risk factor covariates were kept and which were dropped from the final risk adjustment specification. For content focus groups that are explicitly tiered by increasing severity, either all risk factors are included within a content focus group or none of them. For example, if response option levels 1 and 2 for M1800 Grooming were statistically significant at a probability of <0.0001 for a particular outcome, then response option level 3 for M1800 Grooming was added to the list even if it was not statistically significant. If none of the risk factors within an explicitly tiered content focus group was statistically significant at <0.0001, the entire content focus group was removed from the model.
      4. A logistic regression was computed on the list of risk factors kept after Step 3 above. 
      5. Goodness of fit and reliability statistics (McFadden’s R2, C-statistic, and Intra-Class Correlation) were calculated to measure how well the predicted values generated by the prediction model were related to the actual outcomes. Separate bivariate correlations were constructed between the risk factors and the outcomes to confirm the sign and strength of the estimated coefficients in the logistic model. 
      6. The initial model was reviewed by a team of at least three experienced home health clinicians. Each risk factor was reviewed for its clinical plausibility. Clinicians were asked about the direction indicated by the coefficient in the risk adjustment model and how it compares to their perceived bivariate relationship given their experience treating patients in the home. Risk factors that were not clinically plausible were revised or eliminated if revisions were not possible. 
      7. The risk factors that were deemed not clinically plausible were revised or eliminated, and Steps 3, 4, and 5 in this process were repeated. The resulting logistic regression equation was designated as the risk adjustment model for the outcome. 
      8. The risk adjustment model was applied to the validation sample and goodness of fit statistics were computed. The statistics were similar to the goodness of fit statistics computed with the development sample. As additional testing, home health agencies were stratified across several observable characteristics, and the distributions of the risk-adjusted outcomes were checked to confirm that values remained similar across strata.

       

      Using CY 2021 data, the updated risk adjustment model specification yielded a McFadden’s R2 of 0.1894 and a C-Statistic of 0.9098 on the validation sample. Please refer to Appendix A (see supplemental attachment pages 27-31) for details on the risk factor coefficients, including standard deviations and p-values.

       

      [1] https://www.cms.gov/files/document/risk-adjustment-technicalspecifications2024.pdf

      4.4.5 Calibration and Discrimination

      We calibrated the most recent risk adjustment update by comparing changes in performance for home health agencies overall and by important subgroups (urbanicity/rurality, size, and share of quality episodes with non-white patients) to the prior risk adjustment specification. The results in Figure 3 (see supplemental attachment page 22) indicate that most home health agencies overall and by subgroup perform equally well based on the updated risk adjustment model compared to the prior model, ranging between 86 percent among urban home health agencies and 88.6 percent among home health agencies with the highest percentage of non-white patients. 

      4.4.6 Interpretation of Risk Factor Findings

      A patient’s improvement in bed transferring is dependent on a variety of factors, including social risk factors, clinical and behavioral risk factors, and access to care. Risk adjustment is used to promote incentives for home health agencies to provide the same care to patients regardless of patient characteristics at SOC/ROC. All risk factors that are expected to impact the ability to improve bed transferring between SOC/ROC and EOC and are based on OASIS items that will remain or will be added following the transition to OASIS-E are considered for model inclusion. The statistical properties of the OASIS items were examined to specify risk factors (e.g., item responses were grouped when there was low prevalence of certain responses). Team clinicians then reviewed all risk factors for clinical relevance and redefined or updated risk factors, as necessary. These risk factors were divided into 31 content focus groups (e.g., functional status, Hierarchical Condition Categories, etc.). The final risk adjustment model was selected by considering model fit statistics, statistical significance of risk factor coefficients, and clinical plausibility of magnitude and direction of coefficients. Please refer to other sections for details on the risk factor selection methodology.

      4.4.7 Final Approach to Address Risk Factors
      Statistical risk model with risk factors
      Risk adjustment approach
      On
      Risk adjustment approach
      Off
      Conceptual model for risk adjustment
      Off
      Conceptual model for risk adjustment
      On
    • 5.1 Contributions Towards Advancing Health Equity

      Across home health agencies, we compared Improvement in Bed Transferring (#0175) CY 2022 performance by subgroups for urbanicity/rurality, size, and share of quality episodes with non-white patients (see Figure 4 in supplemental attachment page 23). 

       

      We define urbanicity as home health agencies located within a Core-Based Statistical Area (CBSA) as defined by the Office of Management and Budget (OMB). Urban home health agencies perform on average slightly worse at 0.808 than rural home health agencies at 0.831. 

       

      We define large home health agencies as home health agencies with quality episode counts in the top quartile for CY 2022 and small home health agencies as being in the bottom quartile. Large home health agencies perform better at 0.875 than small home health agencies at 0.724.

       

      For “Highest Quartile Non-White” home health agencies, we use the M0140: Race/Ethnicity OASIS item to identify the patient’s race/ethnicity as non-white. Home health agencies in the lowest quartile share of quality episodes with non-white patients perform better at 0.830 than home health agencies in the highest quartile at 0.724.

       

      The results, particularly for home health agency size and percentage of non-white patients, indicate a performance gap across home health agencies by subgroup. CMS is monitoring the persistence of these gaps and investigating next steps for addressing through reevaluated measure specifications or other policies (see https://www.cms.gov/medicare/quality/home-health-quality-reporting-program/home-health-qrp-health-equity for additional resources).

    • 6.2.1 Actions of Measured Entities to Improve Performance

      All home health agencies with at least 20 qualifying quality episodes of care receive quarterly measure reports on all their publicly reported measures. In addition, providers can run on-demand, confidential reports showing individual measure results and national averages, through CMS’ iQIES system. There is an email box that home health agencies may submit questions to as well as a website on which the latest measure updates are posted. The OASIS Guidance Manual describes the OASIS-based reports that are available, report use(s), and provides guidance about OASIS and quality improvement. Home health agencies make use of these reports to monitor and improve the quality of care.

      6.2.2 Feedback on Measure Performance

      Home health agencies receive quarterly measure reports on all their measures. There is an email box that home health agencies may submit questions to as well as a website on which the latest measure updates are posted. Because of the changes made to the OASIS in the OASIS-E version (effectively January 1, 2023), risk models for publicly reported outcome measures have been updated. CMS makes available information about risk models and covariates on its website.

      6.2.3 Consideration of Measure Feedback

      No measures specifications changes requested or made.

      6.2.4 Progress on Improvement

      The measure is important to report publicly. Although improvements in performance are small, home health agencies continue to improve overall, and for each subgroup measured over time. Performance gaps still exist, indicating further improvement is possible. Publicly reported measure results illustrate variation in performance across home health agencies that may inform patient and family choice of a home health agency.

       

      Figure 5 (see supplemental attachment page 26) presents trends in risk-adjusted Improvement in Bed Transferring (#0175) by subgroup. In addition to overall improvement from CY 2019 to CY 2022, each subgroup improves. We expect improvement to be driven in part by the implementation of the Quality of Patient Care (QoPC) Star Rating beginning in July 2015 and the Home Health Value Based Purchasing (HHVBP) Model in 2016. Results prior to 2019 showed dramatic improvement (not shown), while improvement in 2019-2022 was smaller. Nonetheless, QoPC Star Rating and HHVBP still provide incentives for home health agencies to improve on this measure. We anticipate continued improvement as HHVBP expands nationwide in 2023. Data will not reflect this policy change, as we only report results through CY 2022.

      6.2.5 Unexpected Findings

      Recent improvement in this measure has been relatively large compared to historical trends. We believe these large improvements are due to the implementation of two initiatives that involve this measure – the QoPC Star Ratings and HHVBP – beginning in 2015 and 2016. We do not find any unexpected findings during implementation of this measure at this time.

    • First Name
      Olivia
      Last Name
      Giles

      Submitted by Olivia on Tue, 06/11/2024 - 14:47

      Permalink

      Hi! I'm Janice Tufte, patient partner. I didn't want to comment on all of them, but I think they all are important especially now that there's a little bit more investment and recognition about the issues that are going on in care facilities and nursing homes, post-acute care and long-term care facilities. I really feel that they're all something that should be addressed, and so unless I looked in more deeply into some of it, I would support all of these. Improvement in the next one or the 2 after this, I think, is extremely important. So I'm just going to comment on all right now.

      Organization
      Janice Tufte
      First Name
      Olivia
      Last Name
      Giles

      Submitted by Olivia on Tue, 06/11/2024 - 14:48

      Permalink

      I agree with Janice, 100%. All of these issues are very important. I have a close family friend, a childhood friend who's going through these things and she has MS. And they all hit these categories. So, thank you. I support it. This is a very important thing, like I said. [Close friend with MS] is to the point that she feels like there's no cure. She's bedridden and it's very hard to find a person to take care of that. And how it affects the weight of the body on it and the bruising effect of it. It's a very important ruling to have here. So I think it's a must needed.

      Organization
      Shabina Khan
      First Name
      Olivia
      Last Name
      Giles

      Submitted by Olivia on Tue, 06/11/2024 - 14:48

      Permalink

      As a patient myself, I've had a lot of issues with these [activities captured in the measures], and I don't think people realize the significance and the impact that it can have on just our mental health and our care. So, I do want to just say that I appreciate all of these things, especially the bathing initiative. I've had sepsis in the past from a hospitalization. And so, I think, looking at these things proactively, is very important, but just wanted to lend my support and give a voice to this kind of a meeting where not too many people are commenting. So that's it. Thank you.

      Organization
      Christine Von Raesfeld
      First Name
      Olivia
      Last Name
      Giles

      Submitted by Olivia on Tue, 06/11/2024 - 14:49

      Permalink

      Hi Florence, patient partner. So, I'm just hoping that the data that's received from the measures will provide improvement for the patients. My experience being a caregiver, I don't know if this has been implemented or used, that the improvements for the ambulation, bathing, bed transferring, and management of their oral medications made an impact with the people that was providing caregiving for within my family. So, I'm hoping that the data that's being collected will improve their outcomes when it comes to these different measures especially, for what tools that are being used and the services that are being provided and the way they're being measured will actually achieve these outcomes. So that's what I'm hoping that these measures will actually achieve these outcomes. So, I don't think these are new, but we are just reassessing them? 

      • Correct. These four CBE (0167, 0174, 0175, and 0176) are all maintenance measures. 

       

      Maintenance measures. I'm hoping that there have been some additions added to the current maintenance measures that we're reviewing to make improvements from the last maintenance. That the last set of data that was collected, that we'll use that information to make improvements for these new set of measures. So, it's not just someone just providing these services and the patient never making any improvements. I'm hoping that these measurements have been improved so that we can actually see some improvements for the patients.

      • So, what measure changes have been made since the initial endorsement? And then, has there been any information on patient improvements over time? Did I capture that correctly?

       

      Yes.

      Organization
      Florence Thicklin (Committee member for Management of Acute Events and Chronic Conditions)

    • Importance

      Importance Rating
      Met
      Importance

      Strengths:

      • The developer provides a logic model illustrating the process of improvement in bed transferring from SOC/ROC to EOC. The logic model showcases the observed improvements that result from the combined support provided by the patient’s home health agency, caregivers, and the patient’s efforts in self-care
      • The developer highlights the need for assistance with activities of daily living (ADLs), citing evidence that worldwide, 20% of older adults living at home report needing assistance with ADLs. Moreover, in the US, 29% of older adults report facing challenges in performing their ADLs. The developer emphasizes the role of home health care staff in encouraging patients to be independent, teaching patients how to use devises/equipment that assist in safely performing ADLs.
      • The developer provides performance scores by decile: 
        The difference between the minimum (0.021) and maximum (1.000) scores, as well as the range of mean scores across deciles (0.695 to 0.875) suggests variations in performance among entities being measured.
      • The developer highlights a public comment and feedback opportunity on the home health quality reporting program but noted a lack of comments on this measure. 
      • The developer notes the alignment of the measure with the target population and the recognition of functional status as an important domain by a Technical Expert Panel (TEP), suggesting the measure’s utility and meaningfulness to Medicare-certified home health agencies. 

      Limitations: 

      • None identified. 

      Rationale:

      • There is a business case for the measure along with supporting evidence for the importance of the measured outcomes with demonstrated gap in performance.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Strengths: 

      • The developer notes this measure is part of the Home Health Quality Reporting Program and they did not identify any feasibility challenges.
      • The developer reports there were no issues regarding data availability, missing data, timing or frequency of data collection, patient confidentiality, or implementation.
      • The developer confirms that the information on bed transferring status used to calculate this measure is part of the OASIS items embedded in the home health agency’s clinical assessment.
      • OASIS data are collected by the home health agency during the care episode and submitted electronically to CMS via the Internet Quality Improvement and Evaluation System (iQIES).
      • There are no proprietary components of this measure.

      Limitations: 

      • None identified.

      Rationale: 

      • There are no feasibility challenges, fees, or proprietary components of this measure.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Strengths:

      • The measure is clear and well defined. 
        Data element reliability tested on OASIS item M1850 at start of care and at discharge. Kappa statistics are 0.42 and 0.45, respectively.
      • Entity-level reliability is calculated with data from 2022 across 7601 entities. The measure is risk-adjusted and a split-half reliability test is used, adjusted for agency size. Average entity-level reliability is >0.6 for all entity-size deciles. (The estimated reliability for the first decile is 0.695.)

      Limitations:

      • None Identified.

      Rationale: 

      • The measure is well defined. Reliability was assessed at both the patient and entity level. Reliability statistics are above the established thresholds for most, if not all, entities.
      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Strengths: 

      • The developer assessed the convergent validity of the measure by calculating the Spearman Rank Correlations with 3 other OASIS-based measures assessing improvement in patient functioning, hypothesizing and observing strong positive correlations (ρ = 0.7-0.76). The developer notes that this association does not distinguish if strong performance on one measure score leads to strong performance on another score, or if strong performance on multiple scores is a reflection of quality of care.
      • The developer also correlated the measure with the Discharge to Community measure that similarly assesses patient functioning, hypothesizing and observing a positive correlation (ρ = 0.2315).
      • In assessing item validity of the measure, the developer established consensus validity through review by panels of researchers and clinicians. This resulted in a recommendation of the data element for measuring patient outcomes relevant to home health care provision and quality measurement, or for risk adjustment of outcome analyses.
      • Expert clinical panels reviewed the data element and recommended its inclusion in a core set of data items for patient assessment and care planning.
      • The developer tested the data element for its criterion or convergent/predictive validity in measuring outcomes and risk factors. The results revealed statistically significant and clinically meaningful relationship with other indicators of health status and patient outcomes.
      • The developer tested the data element was tested and used in an algorithm that determines payment adjustments to home health agencies per episode of care under the Medicare home health benefit. The developer notes that clinicians across numerous home health agencies report the data element’s efficacy in patient assessment and care planning.
      • The risk adjustment model was developed with 116 factors evaluated for statistical significance and clinical plausibility. C-statistic of 0.9098 indicated strong model performance and stratification of model scores by agency demographic subgroup resulted in similar performance.

      Limitations: 

      • There is potential for biases or limitations in the composition of the expert panels. Additional details on the panel selection and deliberation methods would help asses the robustness of the consensus validity process.
      • The developer notes feedback from clinicians across numerous home health agencies on the efficacy of the data element in patient assessment and care planning but does not specific details of the feedback and real-world use.

      Rationale: 

      • The developer assessed measure validity using accountable entity-level empirical validity and data-element level validity. The interpretation of the empirical results supports an inference of validity. 

      Equity

      Equity Rating
      Met
      Equity

      Strengths: 

      • The developer compared performance for CY 2022 by subgroups for urbanicity/rurality, size, and share of quality episodes with non-white patients.
      • The developer reports that on average, urban home health agencies (0.808) performed slightly worse than rural home health agencies (0.831). 
      • Large home health agencies demonstrated better performance at 0.875 than small home health agencies at 0.724.
      • The developer used the M0140: Race/Ethnicity OASIS item to identify patients’ race/ethnicity as non-white. The developer reports that home health agencies with the lowest quartile share of quality episodes involving non-white patients performed better at 0.830 than those with a higher share at 0.724.

      Limitations:

      • The share of quality episodes in non-white patients oversimplifies the complexity of racial and ethnic categorization. There may be important differences across various racial and ethnic groups that extend beyond the binary classification of white versus non-white.

      Rationale: 

      • The developer evaluated disparities in performance by subgroups for urbanicity/rurality, size, and share of quality episodes with non-white patients.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Strengths:

      • The developer indicates the measure is currently used in CMS’s Public Reporting and Home Health Star Ratings for outcome-based quality improvement.
      • The developer states that home health agencies, with a minimum of 20 qualifying quality episodes of care, receive quarterly measure reports on all their publicly reported measures.
      • Additionally, providers can access confidential reports that detail individual measure results and national averages through CMS’s iQIES system.
      • The developer notes that home health agencies can submit questions via email in addition to accessing the latest measure updates online. Modifications to the OASIS-E version, effective from January 1, 2023, have resulted in updates to risk models for public outcome measures.
      • The developer highlights overall improvement by patient subgroup from CY 2019 (0.774) to 2022 (0.810) with each subgroup demonstrating improvement.
      • The developer cites the implementation of the Quality of Patient Care (QoPC) Star Rating and the Home Health Value Based Purchasing (HHVBP) Model as potential drivers for the observed improvements.
      • The developer did not identify any unexpected findings.

      Limitations:

      • None identified.

      Rationale:

      • While the rate of improvement has slowed down in recent years, the developer provides data demonstrating overall improvement in the measure. The developer acknowledges the existence of performance gaps and anticipates further improvement with the nationwide expansion of HHVBP.
    • First Name
      Andrew
      Last Name
      Kohler

      Submitted by Andrew on Tue, 06/25/2024 - 16:56

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Addressed

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Addressed

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Addressed

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Addressed

      Equity

      Equity Rating
      Met
      Equity

      Addressed

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Addressed

      Summary

      Subsection met

      First Name
      Margherita
      Last Name
      Labson

      Submitted by Margherita C Labson on Wed, 06/26/2024 - 11:34

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Given the relationship of bed transfers to falls in the home and the deleterious sequelae that follows, the business case for this measure is solid.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Measure is clearly defined and well understood in the industry

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      No further comment

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      No further comment

      Equity

      Equity Rating
      Met
      Equity

      Agree with staff comments regarding the limitations identified during their review.  Concurs with this reviewer's experiential knowledge.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      No further comment

      Summary

      No further comment

      First Name
      Stephen
      Last Name
      Weed

      Submitted by Stephen Weed on Fri, 06/28/2024 - 15:51

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      The medical issues associated with falls and a reduction in ambulatory ability are closely tied. The measure does not link these per se but the case for monitoring both is well documented. 

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Addressed

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Addressed

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Addressed

      Equity

      Equity Rating
      Met
      Equity

      The evaluation of equity is addressed well in the full measure. Just as in the mobility measure, it is important to consider what can be done for the diverse medical subsets of those receiving in home services. It's disheartening to see those with more advanced medical conditions with a higher incidence of falls. I have family members in a residential facility and the failure of staff to  prevent falls for those most vulnerable....well I hope you understand what I am feeling without enunciating it. 

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Addressed

      Summary

      This measure is like the ambulatory measure- 0167 in it's importance. 

      First Name
      Karie
      Last Name
      Fugate

      Submitted by Karie Fugate on Thu, 07/04/2024 - 12:29

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      As a patient and caregiver, and having dealt with Home Health Care for extended family members, this is a vital measure for patients.

       

      The developer has captured the importance of the “Improvement in bed transferring” quality measure very well: 

      • A large part of work and support in home health is around safety in the home and the safe performance of activities of daily living (ADLs) including transferring in and out of bed. 
      • Home health care staff can encourage patients to be as independent as possible, can evaluate patients´ needs, and can teach them how to use special devices or equipment that assist them in the process of safely getting in and out of bed.
      • Improving functional status related to bed transferring contributes to quality of life and can allow patients to live as long as possible in their own environment.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Per the developer, this is a long-standing measure in the Home Health Quality Reporting Program and no feasibility issues have been identified. OASIS data collection and submission are a requirement of the Medicare Home Health Conditions of Participation.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      I agree that the measure is clear and well defined even though reading the full measure submission was complex for a patient/caregiver. 

       

      This is a long-standing measure in the Home Health Quality Reporting Program. The measure is calculated by comparing patient functioning at the start and end of a home health quality episode, as reported by the home health OASIS system. It appears that data has been collected and validated as far back as 2001.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Per the developer: OASIS data items were developed, tested in hundreds of agencies, and refined for measuring outcomes to evaluate and enhance the effectiveness of home care. OASIS data items and measurement methods were reviewed by multidisciplinary panels of research methodologists, clinicians, home care managers, and policy analysts. Several tests of validity were conducted for each OASIS item.

      Equity

      Equity Rating
      Met
      Equity

      As a patient/caregiver I struggled with this part of the quality measure. I also went out and reviewed the CMS website provided by the developer on Home Health QRP Health Equity. 

       

      The developer notes: 

      • The results, particularly for home health agency size and percentage of non-white patients, indicate a performance gap across home health agencies by subgroup. CMS is monitoring the persistence of these gaps and investigating next steps for addressing through reevaluated measure specifications or other policies.

       

      The CMS.gov website states: Health equity is defined as the attainment of the highest level of health for all people. 

       

      It was difficult to figure out how the data would indicate a performance gap across home health agencies if performance data is not discussed for all by age, sex, race (all) and then broken out in urban/rural and large versus small home health agencies. It would also be interesting to see reasons why patients got worse instead of staying the same or getting better (to me, this would be a good indicator to see if this is common for all races, sex, region, condition, etc.).

       

      Since CMS seems behind on providing more timely data, and they are working the persistence of gaps and investigating next steps, I believe that next cycle will be more robust with data being broken out.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Per the developer, this is a long-standing measure in the Home Health Quality Reporting Program. OASIS data collection and submission are a requirement of the Medicare Home Health Conditions of Participation.

      Summary

      As a patient and caregiver, and having dealt with Home Health Care for extended family members, this is a vital measure for patients.

       

      The developer has captured the importance of the “Improvement in bed transferring” quality measure very well.

      First Name
      Carol
      Last Name
      Siebert

      Submitted by Carol Siebert on Sat, 07/06/2024 - 21:17

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Agree with staff preliminary assessment

      However, the listening session comments reminded me that the primary OASIS data set item used to inform this measure really does not measure “getting in and out of bed” as most people understand that activity.  

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Agree with staff preliminary assessment

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Not met but addressable
      Scientific Acceptability Reliability

      Do not agree with staff preliminary assessment. 
      I'd like to address the reliability of the underlying data element, with 0.42 kappa at start/resumption of care and 0.45 at discharge, with home health clinicians trained by the Abt study team and retesting within 24 hours. This particular OASIS item has one of the most complicated sets of instructions of any OASIS items, and certainly of those related to patient performance. It presumes to measure a performance moving from supine to sitting on side of bed to (maybe) standing and pivoting and descending into another (maybe) nearby chair and then reversing the maneuver. If there is no chair or wheelchair nearby, it can be in another room. As an occupational therapist who has assessed or addressed bed transfers with probably 10,000 patients in the home, and was involved for years in teaching clinicians how to score OASIS items accurately, I’m not surprised that kappa values at SOC/ROC and at discharge are so low. I do not think this is acceptable, and I think the ambiguity of the instructions for the data item contributes significantly to the reliability of the item. I think this data item could be revised to clean up the ambiguity of the specified task being scored.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      agree with preliminary staff assessment

      Equity

      Equity Rating
      Not met but addressable
      Equity

      There are equity problems inherent in the underlying data item as well as in the subgroup testing. The data item assumes that the person has a bed and can access the bed.  There is no option to account for those who have no bed, but instead use a recliner, couch, or a mat or futon on the floor for sleeping. As to the subgroup testing, the bed alternatives I just listed may be related to cultural or economic circumstances or both. Is it possible to do subgroup testing that captures economic status? I realize that requires a more sophisticated analysis (e.g. by zip code or census tract). But I also know that one of the easiest ways to improve this item score by at least one value is with an assistive device that costs about $50 and is not covered by Medicare, Medicaid or any other insurance. This item is one where “improvement” can track with the patient or household financial status.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Agree with preliminary staff assessment.

      Summary

       For those seeking quality home health services for a loved one, a key question is “will my loved one be able to return home and manage the things they are used to managing?” While clinicians may "slice and dice" performance and assistance in fine increments, for loved ones the gradations are more stark: can they do this by themselves, does someone have to be there, does someone have to help, or does someone have to do it for them? One of the strengths of the OASIS functional items since inception has been that the scoring tracked more closely to intensity of caregiving rather than fine gradations of assistance. Some items distinguish between performance without use of assistive devices vs needing the use of a device to complete the specified task. But this is the only such item that scores use of a device OR human assistance in a single score.  In the importance section, the developer cites a study (Anderson & Wiener, 2015) indicating that the use of all kinds of technologies is reducing the need for personal assistance. I would encourage revision of this data itme, either to include use of technology within the “0” score, or to include technology in the “1” score and minimal human assistance as a “2.” Needing a device only compared to needing human assistance present very different situations in terms of caregiving and, potentially, whether a person can remain in their home. The underlying data item should not consider them equivalent.

      First Name
      Matthew
      Last Name
      Galchutt

      Submitted by paul_galchutt@… on Mon, 07/08/2024 - 16:08

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      -

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      -

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      -

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      -

      Equity

      Equity Rating
      Met
      Equity

      -

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      -

      Summary

      Thank you for the continued clarity concerning the key points related to this measure. 

      First Name
      Morris
      Last Name
      Hamilton

      Submitted by Morris Hamilton01 on Mon, 07/08/2024 - 19:20

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      I agree with staff preliminary assessment. As a former member of the Measure Developer team, I am conflicted from voting on this measure.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      I agree with staff preliminary assessment. As a former member of the Measure Developer team, I am conflicted from voting on this measure.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      I agree with staff preliminary assessment. As a former member of the Measure Developer team, I am conflicted from voting on this measure.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      I agree with staff preliminary assessment. As a former member of the Measure Developer team, I am conflicted from voting on this measure.

      Equity

      Equity Rating
      Met
      Equity

      I agree with staff preliminary assessment. As a former member of the Measure Developer team, I am conflicted from voting on this measure.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      I agree with staff preliminary assessment. As a former member of the Measure Developer team, I am conflicted from voting on this measure.

      Summary

      I agree with staff preliminary assessment. As a former member of the Measure Developer team, I am conflicted from voting on this measure.